Overview

Dataset statistics

Number of variables22
Number of observations105395
Missing cells827337
Missing cells (%)35.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.5 MiB
Average record size in memory244.0 B

Variable types

NUM12
UNSUPPORTED7
CAT2
BOOL1

Warnings

operation_car has constant value "105395" Constant
operation_date has a high cardinality: 17065 distinct values High cardinality
index_train has 105395 (100.0%) missing values Missing
danger has 88907 (84.4%) missing values Missing
loaded has 105395 (100.0%) missing values Missing
operation_train has 105395 (100.0%) missing values Missing
rod_train has 105395 (100.0%) missing values Missing
ssp_station_esr has 105395 (100.0%) missing values Missing
ssp_station_id has 105395 (100.0%) missing values Missing
weight_brutto has 105395 (100.0%) missing values Missing
adm is highly skewed (γ1 = 40.75622229) Skewed
df_index has unique values Unique
index_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
loaded is an unsupported type, check if it needs cleaning or further analysis Unsupported
operation_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
rod_train is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_esr is an unsupported type, check if it needs cleaning or further analysis Unsupported
ssp_station_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
weight_brutto is an unsupported type, check if it needs cleaning or further analysis Unsupported
receiver has 16781 (15.9%) zeros Zeros

Reproduction

Analysis started2021-04-14 20:03:11.442945
Analysis finished2021-04-14 20:03:47.663612
Duration36.22 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct105395
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2103808.687
Minimum8
Maximum4189794
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:47.802922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile195002.9
Q11063153
median2046369
Q33178158.5
95-th percentile4017198.3
Maximum4189794
Range4189786
Interquartile range (IQR)2115005.5

Descriptive statistics

Standard deviation1220516.961
Coefficient of variation (CV)0.5801463642
Kurtosis-1.180530781
Mean2103808.687
Median Absolute Deviation (MAD)1041859
Skewness0.04417338718
Sum2.217309166e+11
Variance1.489661651e+12
MonotocityStrictly increasing
2021-04-14T23:03:47.948694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15749131< 0.1%
 
40259761< 0.1%
 
24920531< 0.1%
 
5191541< 0.1%
 
20271441< 0.1%
 
41038141< 0.1%
 
8629601< 0.1%
 
1798421< 0.1%
 
884191< 0.1%
 
34975981< 0.1%
 
958671< 0.1%
 
38503141< 0.1%
 
1461251< 0.1%
 
6734371< 0.1%
 
37515381< 0.1%
 
24367201< 0.1%
 
41589141< 0.1%
 
27398201< 0.1%
 
24858641< 0.1%
 
32456691< 0.1%
 
28722881< 0.1%
 
40423361< 0.1%
 
26435511< 0.1%
 
39563181< 0.1%
 
22483481< 0.1%
 
Other values (105370)105370> 99.9%
 
ValueCountFrequency (%) 
81< 0.1%
 
541< 0.1%
 
1701< 0.1%
 
1731< 0.1%
 
2021< 0.1%
 
2521< 0.1%
 
3721< 0.1%
 
3881< 0.1%
 
4611< 0.1%
 
4961< 0.1%
 
ValueCountFrequency (%) 
41897941< 0.1%
 
41897411< 0.1%
 
41896351< 0.1%
 
41895811< 0.1%
 
41895661< 0.1%
 
41895301< 0.1%
 
41895141< 0.1%
 
41894941< 0.1%
 
41894841< 0.1%
 
41894361< 0.1%
 

index_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

length
Real number (ℝ≥0)

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.011785853
Minimum0.78
Maximum2.13
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:48.105252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.78
5-th percentile0.87
Q11
median1
Q31
95-th percentile1.32
Maximum2.13
Range1.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1536514365
Coefficient of variation (CV)0.1518616178
Kurtosis14.46997813
Mean1.011785853
Median Absolute Deviation (MAD)0
Skewness3.401341235
Sum106637.17
Variance0.02360876394
MonotocityNot monotonic
2021-04-14T23:03:48.268186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
16609562.7%
 
0.871678015.9%
 
1.0691778.7%
 
0.8532593.1%
 
1.2213541.3%
 
1.3612851.2%
 
1.857640.7%
 
1.827180.7%
 
1.417120.7%
 
1.016840.6%
 
0.866160.6%
 
1.114930.5%
 
1.323860.4%
 
0.833550.3%
 
1.273410.3%
 
1.63410.3%
 
1.673330.3%
 
1.033020.3%
 
1.352810.3%
 
1.052280.2%
 
1.832190.2%
 
0.791320.1%
 
1.731160.1%
 
0.91030.1%
 
1.71790.1%
 
Other values (21)2420.2%
 
ValueCountFrequency (%) 
0.784< 0.1%
 
0.791320.1%
 
0.821< 0.1%
 
0.833550.3%
 
0.8532593.1%
 
0.866160.6%
 
0.871678015.9%
 
0.91030.1%
 
0.922< 0.1%
 
0.996< 0.1%
 
ValueCountFrequency (%) 
2.132< 0.1%
 
1.9213< 0.1%
 
1.8912< 0.1%
 
1.857640.7%
 
1.842< 0.1%
 
1.832190.2%
 
1.827180.7%
 
1.7717< 0.1%
 
1.758< 0.1%
 
1.731160.1%
 

car_number
Real number (ℝ≥0)

Distinct83016
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60277175.58
Minimum24051609
Maximum98098866
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:48.459669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24051609
5-th percentile50064872
Q153717187
median57283392
Q362878905
95-th percentile93939820.7
Maximum98098866
Range74047257
Interquartile range (IQR)9161718

Descriptive statistics

Standard deviation12788218.5
Coefficient of variation (CV)0.2121568964
Kurtosis2.476725353
Mean60277175.58
Median Absolute Deviation (MAD)4529067
Skewness1.183226314
Sum6.35291292e+12
Variance1.635385323e+14
MonotocityNot monotonic
2021-04-14T23:03:48.613240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5586482124< 0.1%
 
5592753723< 0.1%
 
5582294422< 0.1%
 
5570113022< 0.1%
 
5562642822< 0.1%
 
5582292822< 0.1%
 
5586422721< 0.1%
 
3202040620< 0.1%
 
5599793618< 0.1%
 
5586486217< 0.1%
 
5586471417< 0.1%
 
3416448317< 0.1%
 
3416447517< 0.1%
 
5585181017< 0.1%
 
5595435817< 0.1%
 
5595448117< 0.1%
 
3416141417< 0.1%
 
5592452617< 0.1%
 
5595255017< 0.1%
 
5586446617< 0.1%
 
3416593617< 0.1%
 
3202045517< 0.1%
 
3415577017< 0.1%
 
5575018617< 0.1%
 
3202025717< 0.1%
 
Other values (82991)10492999.6%
 
ValueCountFrequency (%) 
240516091< 0.1%
 
240775881< 0.1%
 
241739401< 0.1%
 
241879241< 0.1%
 
241970141< 0.1%
 
241980121< 0.1%
 
242209311< 0.1%
 
242692501< 0.1%
 
242869571< 0.1%
 
243065081< 0.1%
 
ValueCountFrequency (%) 
980988661< 0.1%
 
980987421< 0.1%
 
980987002< 0.1%
 
980984291< 0.1%
 
980983462< 0.1%
 
980982961< 0.1%
 
980982211< 0.1%
 
980982131< 0.1%
 
980981711< 0.1%
 
980981141< 0.1%
 

destination_esr
Real number (ℝ≥0)

Distinct1130
Distinct (%)1.1%
Missing624
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean855538.1576
Minimum10002
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:48.783805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10002
5-th percentile249302
Q1852002
median925701
Q3970001
95-th percentile989309
Maximum998100
Range988098
Interquartile range (IQR)117999

Descriptive statistics

Standard deviation206440.2828
Coefficient of variation (CV)0.2412987439
Kurtosis7.188083939
Mean855538.1576
Median Absolute Deviation (MAD)57813
Skewness-2.755956288
Sum8.963558832e+10
Variance4.261759035e+10
MonotocityNot monotonic
2021-04-14T23:03:49.108897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
94700554885.2%
 
98930954145.1%
 
88380942934.1%
 
96780834733.3%
 
92570130532.9%
 
85200228712.7%
 
98550525582.4%
 
80120825402.4%
 
86230522012.1%
 
98780121572.0%
 
98610319991.9%
 
98570219771.9%
 
94400718951.8%
 
84010917231.6%
 
88780016481.6%
 
98351415291.5%
 
52100114561.4%
 
81760014131.3%
 
95470413621.3%
 
86420713471.3%
 
7640412721.2%
 
94210512571.2%
 
89180612281.2%
 
98470011121.1%
 
89210311001.0%
 
Other values (1105)4840545.9%
 
ValueCountFrequency (%) 
100022< 0.1%
 
103032< 0.1%
 
118043< 0.1%
 
149062< 0.1%
 
154001< 0.1%
 
1580511< 0.1%
 
164031< 0.1%
 
170014< 0.1%
 
179045< 0.1%
 
1840914< 0.1%
 
ValueCountFrequency (%) 
998100730.1%
 
9969041< 0.1%
 
9963028< 0.1%
 
9933041420.1%
 
99310729< 0.1%
 
9912053< 0.1%
 
991101540.1%
 
9907008< 0.1%
 
9906074< 0.1%
 
99000514< 0.1%
 

adm
Real number (ℝ≥0)

SKEWED

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.11079273
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:49.248524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median20
Q320
95-th percentile20
Maximum99
Range79
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.519491096
Coefficient of variation (CV)0.07555600202
Kurtosis2069.027137
Mean20.11079273
Median Absolute Deviation (MAD)0
Skewness40.75622229
Sum2119577
Variance2.308853192
MonotocityNot monotonic
2021-04-14T23:03:49.357232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
2010376498.5%
 
2611381.1%
 
273250.3%
 
211170.1%
 
9930< 0.1%
 
2513< 0.1%
 
225< 0.1%
 
243< 0.1%
 
ValueCountFrequency (%) 
2010376498.5%
 
211170.1%
 
225< 0.1%
 
243< 0.1%
 
2513< 0.1%
 
2611381.1%
 
273250.3%
 
9930< 0.1%
 
ValueCountFrequency (%) 
9930< 0.1%
 
273250.3%
 
2611381.1%
 
2513< 0.1%
 
243< 0.1%
 
225< 0.1%
 
211170.1%
 
2010376498.5%
 

danger
Boolean

MISSING

Distinct1
Distinct (%)< 0.1%
Missing88907
Missing (%)84.4%
Memory size823.5 KiB
1
16488 
(Missing)
88907 
ValueCountFrequency (%) 
11648815.6%
 
(Missing)8890784.4%
 
2021-04-14T23:03:49.451019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

gruz
Real number (ℝ≥0)

Distinct360
Distinct (%)0.3%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean190397.6961
Minimum3009
Maximum999993
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:49.541742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3009
5-th percentile81188
Q1151446
median161096
Q3221136
95-th percentile331016
Maximum999993
Range996984
Interquartile range (IQR)69690

Descriptive statistics

Standard deviation96117.23825
Coefficient of variation (CV)0.5048235363
Kurtosis6.842201859
Mean190397.6961
Median Absolute Deviation (MAD)20004
Skewness1.889233573
Sum2.00656324e+10
Variance9238523489
MonotocityNot monotonic
2021-04-14T23:03:49.699317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1610961813817.2%
 
14109277327.3%
 
16104351514.9%
 
9111845734.3%
 
16111343694.1%
 
16113235663.4%
 
21403934653.3%
 
16112831543.0%
 
21105629952.8%
 
14116229052.8%
 
22113628672.7%
 
16101627742.6%
 
15144627722.6%
 
28104824922.4%
 
8118824762.3%
 
31405922962.2%
 
300922312.1%
 
22106620421.9%
 
39149816961.6%
 
32411616531.6%
 
16106214241.4%
 
23603811521.1%
 
23243110641.0%
 
33101610111.0%
 
3160739120.9%
 
Other values (335)2047819.4%
 
ValueCountFrequency (%) 
300922312.1%
 
110058670.8%
 
120083< 0.1%
 
1300031< 0.1%
 
140031020.1%
 
150063< 0.1%
 
1801913< 0.1%
 
180234110.4%
 
181088< 0.1%
 
210795< 0.1%
 
ValueCountFrequency (%) 
9999936< 0.1%
 
7573251< 0.1%
 
7560631< 0.1%
 
7310623< 0.1%
 
72550216< 0.1%
 
7210411< 0.1%
 
7113178< 0.1%
 
71128546< 0.1%
 
71126620< 0.1%
 
711035790.1%
 

loaded
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

operation_car
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size823.5 KiB
11
105395 
ValueCountFrequency (%) 
11105395100.0%
 
2021-04-14T23:03:49.843969image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-04-14T23:03:49.926742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:50.004502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
121079050.0%
 
.10539525.0%
 
010539525.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number31618575.0%
 
Other Punctuation10539525.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
121079066.7%
 
010539533.3%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.105395100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common421580100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
121079050.0%
 
.10539525.0%
 
010539525.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII421580100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
121079050.0%
 
.10539525.0%
 
010539525.0%
 

operation_date
Categorical

HIGH CARDINALITY

Distinct17065
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Memory size823.5 KiB
2020-07-18 15:10:00
 
156
2020-07-10 16:14:00
 
139
2020-07-15 17:34:00
 
129
2020-07-26 12:57:00
 
128
2020-07-16 16:31:00
 
118
Other values (17060)
104725 
ValueCountFrequency (%) 
2020-07-18 15:10:001560.1%
 
2020-07-10 16:14:001390.1%
 
2020-07-15 17:34:001290.1%
 
2020-07-26 12:57:001280.1%
 
2020-07-16 16:31:001180.1%
 
2020-07-17 17:21:001150.1%
 
2020-07-09 17:22:001120.1%
 
2020-07-27 11:13:001120.1%
 
2020-07-17 14:26:001120.1%
 
2020-07-11 17:08:001090.1%
 
2020-07-17 16:22:001090.1%
 
2020-07-23 16:53:001050.1%
 
2020-07-23 13:09:001050.1%
 
2020-07-16 14:46:001020.1%
 
2020-07-31 18:55:001010.1%
 
2020-07-20 17:55:001000.1%
 
2020-07-10 16:52:00970.1%
 
2020-07-18 10:48:00970.1%
 
2020-07-20 16:23:00970.1%
 
2020-07-27 17:08:00950.1%
 
2020-07-27 16:53:00950.1%
 
2020-07-25 15:39:00900.1%
 
2020-07-16 01:35:00890.1%
 
2020-07-14 14:39:00880.1%
 
2020-07-11 15:58:00880.1%
 
Other values (17040)10270797.4%
 
2021-04-14T23:03:50.200976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Frequencies of value counts

Unique

Unique7225 ?
Unique (%)6.9%
2021-04-14T23:03:50.357687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories4 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
061109830.5%
 
231009715.5%
 
-21079010.5%
 
:21079010.5%
 
11561357.8%
 
71462127.3%
 
1053955.3%
 
5538762.7%
 
3534542.7%
 
4444552.2%
 
6401842.0%
 
9309401.5%
 
8290791.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number147553073.7%
 
Dash Punctuation21079010.5%
 
Other Punctuation21079010.5%
 
Space Separator1053955.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
061109841.4%
 
231009721.0%
 
115613510.6%
 
71462129.9%
 
5538763.7%
 
3534543.6%
 
4444553.0%
 
6401842.7%
 
9309402.1%
 
8290792.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-210790100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
105395100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
:210790100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common2002505100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
061109830.5%
 
231009715.5%
 
-21079010.5%
 
:21079010.5%
 
11561357.8%
 
71462127.3%
 
1053955.3%
 
5538762.7%
 
3534542.7%
 
4444552.2%
 
6401842.0%
 
9309401.5%
 
8290791.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2002505100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
061109830.5%
 
231009715.5%
 
-21079010.5%
 
:21079010.5%
 
11561357.8%
 
71462127.3%
 
1053955.3%
 
5538762.7%
 
3534542.7%
 
4444552.2%
 
6401842.0%
 
9309401.5%
 
8290791.5%
 

operation_st_esr
Real number (ℝ≥0)

Distinct386
Distinct (%)0.4%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean905907.0251
Minimum830107
Maximum998100
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:50.486368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum830107
5-th percentile841703
Q1881408
median894109
Q3932705
95-th percentile971502
Maximum998100
Range167993
Interquartile range (IQR)51297

Descriptive statistics

Standard deviation39481.62349
Coefficient of variation (CV)0.04358242336
Kurtosis-0.805312884
Mean905907.0251
Median Absolute Deviation (MAD)29902
Skewness0.1184575704
Sum9.546901184e+10
Variance1558798593
MonotocityNot monotonic
2021-04-14T23:03:50.645479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
8931061122710.7%
 
86420752034.9%
 
88140851614.9%
 
83150438183.6%
 
91320635943.4%
 
91720733553.2%
 
91160533223.2%
 
96870732983.1%
 
92620629062.8%
 
96160428672.7%
 
85280127792.6%
 
88490627742.6%
 
94470223682.2%
 
86130221462.0%
 
92570120882.0%
 
94460917101.6%
 
88790417101.6%
 
86230514031.3%
 
97440713711.3%
 
95540612611.2%
 
88250612491.2%
 
96010311151.1%
 
88760310871.0%
 
9417049500.9%
 
8838098580.8%
 
Other values (361)3576533.9%
 
ValueCountFrequency (%) 
830107770.1%
 
83020028< 0.1%
 
8303041240.1%
 
8307092850.3%
 
83120322< 0.1%
 
831400750.1%
 
83150438183.6%
 
83160825< 0.1%
 
8318059< 0.1%
 
83200912< 0.1%
 
ValueCountFrequency (%) 
9981007< 0.1%
 
996904800.1%
 
99000539< 0.1%
 
9889083530.3%
 
98830628< 0.1%
 
9881091< 0.1%
 
9879052< 0.1%
 
98730348< 0.1%
 
98530831< 0.1%
 
9845024050.4%
 

operation_st_id
Real number (ℝ≥0)

Distinct386
Distinct (%)0.4%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2000463875
Minimum2000035090
Maximum2002025611
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:50.823007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2000035090
5-th percentile2000035194
Q12000035966
median2000037038
Q32000038832
95-th percentile2001933226
Maximum2002025611
Range1990521
Interquartile range (IQR)2866

Descriptive statistics

Standard deviation791682.9162
Coefficient of variation (CV)0.0003957496689
Kurtosis-0.2725703222
Mean2000463875
Median Absolute Deviation (MAD)1354
Skewness1.314307336
Sum2.108188855e+14
Variance6.267618399e+11
MonotocityNot monotonic
2021-04-14T23:03:50.995582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20000359661122710.7%
 
200193081652034.9%
 
200003519451614.9%
 
200193053438183.6%
 
200003635635943.4%
 
200003642433553.2%
 
200003634233223.2%
 
200003862032983.1%
 
200003688829062.8%
 
200003841028672.7%
 
200193347627792.6%
 
200003532427742.6%
 
200003781623682.2%
 
200193077021462.0%
 
200003686820882.0%
 
200003556417101.6%
 
200003780817101.6%
 
200193077814031.3%
 
200003876213711.3%
 
200003830212611.2%
 
200003523212491.2%
 
200003837211151.1%
 
200003553010871.0%
 
20000376629500.9%
 
20000352528580.8%
 
Other values (361)3576533.9%
 
ValueCountFrequency (%) 
20000350904< 0.1%
 
20000351101110.1%
 
200003513025< 0.1%
 
2000035140570.1%
 
20000351624< 0.1%
 
20000351825< 0.1%
 
200003519451614.9%
 
20000352129< 0.1%
 
20000352189< 0.1%
 
200003522233< 0.1%
 
ValueCountFrequency (%) 
20020256118< 0.1%
 
200202560913< 0.1%
 
20020235031< 0.1%
 
20019335384760.5%
 
200193353012< 0.1%
 
2001933522650.1%
 
20019335021350.1%
 
20019334981< 0.1%
 
2001933494710.1%
 
20019334846280.6%
 

operation_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

receiver
Real number (ℝ≥0)

ZEROS

Distinct1918
Distinct (%)1.8%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean27951350.75
Minimum0
Maximum99803052
Zeros16781
Zeros (%)15.9%
Memory size823.5 KiB
2021-04-14T23:03:51.190025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1186507
median5785164
Q361946814
95-th percentile95266723
Maximum99803052
Range99803052
Interquartile range (IQR)61760307

Descriptive statistics

Standard deviation34431644.65
Coefficient of variation (CV)1.231841887
Kurtosis-0.9417545309
Mean27951350.75
Median Absolute Deviation (MAD)5785164
Skewness0.8312485999
Sum2.945736953e+12
Variance1.185538154e+15
MonotocityNot monotonic
2021-04-14T23:03:51.355582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01678115.9%
 
578516442154.0%
 
7311603534093.2%
 
18672029192.8%
 
7960128626432.5%
 
18646525362.4%
 
575767622512.1%
 
7147920721832.1%
 
112664821572.0%
 
16020621432.0%
 
1262761518951.8%
 
9526672316401.6%
 
112663116031.5%
 
2077056215291.5%
 
9705952014871.4%
 
18642414031.3%
 
462269013701.3%
 
112616313231.3%
 
46137912601.2%
 
7442176311761.1%
 
10545711761.1%
 
4785990711631.1%
 
112602210941.0%
 
112601610491.0%
 
9473735610341.0%
 
Other values (1893)4394941.7%
 
ValueCountFrequency (%) 
01678115.9%
 
185951140.1%
 
832623< 0.1%
 
1051821< 0.1%
 
1051991240.1%
 
1052071300.1%
 
1052133080.3%
 
105236610.1%
 
1054141< 0.1%
 
10545711761.1%
 
ValueCountFrequency (%) 
998030521< 0.1%
 
997695851< 0.1%
 
994262301< 0.1%
 
993328429< 0.1%
 
992942833< 0.1%
 
990299602< 0.1%
 
988919994< 0.1%
 
987804901< 0.1%
 
987681584< 0.1%
 
9875445234< 0.1%
 

rodvag
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.0273068
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:51.491258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q160
median60
Q370
95-th percentile95
Maximum99
Range79
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.28810658
Coefficient of variation (CV)0.2387747875
Kurtosis1.856866053
Mean64.0273068
Median Absolute Deviation (MAD)0
Skewness-0.1499185088
Sum6748158
Variance233.7262027
MonotocityNot monotonic
2021-04-14T23:03:51.604915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
606624162.9%
 
701560814.8%
 
9087998.3%
 
2043484.1%
 
9639203.7%
 
4033503.2%
 
9514471.4%
 
9310051.0%
 
924190.4%
 
872560.2%
 
992< 0.1%
 
ValueCountFrequency (%) 
2043484.1%
 
4033503.2%
 
606624162.9%
 
701560814.8%
 
872560.2%
 
9087998.3%
 
924190.4%
 
9310051.0%
 
9514471.4%
 
9639203.7%
 
ValueCountFrequency (%) 
992< 0.1%
 
9639203.7%
 
9514471.4%
 
9310051.0%
 
924190.4%
 
9087998.3%
 
872560.2%
 
701560814.8%
 
606624162.9%
 
4033503.2%
 

rod_train
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

sender
Real number (ℝ≥0)

Distinct933
Distinct (%)0.9%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean38581587.72
Minimum0
Maximum99863723
Zeros355
Zeros (%)0.3%
Memory size823.5 KiB
2021-04-14T23:03:51.756069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile161246
Q14788410
median36836748
Q374877457
95-th percentile94535486
Maximum99863723
Range99863723
Interquartile range (IQR)70089047

Descriptive statistics

Standard deviation34388017.35
Coefficient of variation (CV)0.8913064336
Kurtosis-1.490371587
Mean38581587.72
Median Absolute Deviation (MAD)35777730
Skewness0.2494814406
Sum4.066036367e+12
Variance1.182535738e+15
MonotocityNot monotonic
2021-04-14T23:03:51.911654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
812135971119510.6%
 
4813418793108.8%
 
575767655115.2%
 
18672049924.7%
 
9877051137453.6%
 
1314127435603.4%
 
16020635443.4%
 
578516434203.2%
 
16124633533.2%
 
16187833213.2%
 
7384489828562.7%
 
5547282628392.7%
 
7553387223682.2%
 
16451717101.6%
 
5761598015771.5%
 
28275413791.3%
 
2663568711331.1%
 
343420711121.1%
 
1905314010801.0%
 
492162899940.9%
 
784654219910.9%
 
587349949500.9%
 
76210607840.7%
 
530867346740.6%
 
444746680.6%
 
Other values (908)3232230.7%
 
ValueCountFrequency (%) 
03550.3%
 
444746680.6%
 
832623< 0.1%
 
1052361< 0.1%
 
10870814< 0.1%
 
1097831< 0.1%
 
1600283< 0.1%
 
16020635443.4%
 
16021225< 0.1%
 
1611862< 0.1%
 
ValueCountFrequency (%) 
9986372310< 0.1%
 
9943515717< 0.1%
 
994175151< 0.1%
 
994154911940.2%
 
9877051137453.6%
 
981029914400.4%
 
9772004326< 0.1%
 
977170588< 0.1%
 
976890143< 0.1%
 
976793812600.2%
 

ssp_station_esr
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

ssp_station_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

tare_weight
Real number (ℝ≥0)

Distinct214
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.4924807
Minimum178
Maximum590
Zeros0
Zeros (%)0.0%
Memory size823.5 KiB
2021-04-14T23:03:52.067744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum178
5-th percentile223
Q1235
median240
Q3249
95-th percentile272
Maximum590
Range412
Interquartile range (IQR)14

Descriptive statistics

Standard deviation22.01931227
Coefficient of variation (CV)0.09006130663
Kurtosis70.08852525
Mean244.4924807
Median Absolute Deviation (MAD)7
Skewness5.398951614
Sum25768285
Variance484.8501128
MonotocityNot monotonic
2021-04-14T23:03:52.211323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2401144810.9%
 
24582077.8%
 
23561555.8%
 
23836003.4%
 
23333923.2%
 
24333653.2%
 
23633023.1%
 
24731283.0%
 
22530622.9%
 
23727062.6%
 
23425782.4%
 
24224802.4%
 
24124692.3%
 
23922642.1%
 
25021962.1%
 
24821932.1%
 
26020862.0%
 
24419851.9%
 
23019051.8%
 
27018381.7%
 
24616491.6%
 
26716441.6%
 
26616261.5%
 
23216061.5%
 
22416011.5%
 
Other values (189)2691025.5%
 
ValueCountFrequency (%) 
1787< 0.1%
 
1794< 0.1%
 
18011< 0.1%
 
1818< 0.1%
 
1822< 0.1%
 
1832< 0.1%
 
18426< 0.1%
 
1856< 0.1%
 
1868< 0.1%
 
1875< 0.1%
 
ValueCountFrequency (%) 
5902< 0.1%
 
58847< 0.1%
 
58731< 0.1%
 
58615< 0.1%
 
58513< 0.1%
 
5846< 0.1%
 
5831< 0.1%
 
5801< 0.1%
 
4752< 0.1%
 
4591< 0.1%
 

weight_brutto
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing105395
Missing (%)100.0%
Memory size823.5 KiB

Interactions

2021-04-14T23:03:19.553676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:19.707228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:19.873816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:20.051349image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:20.214910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:20.390426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:20.550995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:20.722081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:20.900609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:21.061783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:21.235919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:21.392504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:21.543093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:21.701637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:21.868229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:22.050741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:22.214266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:22.387801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:22.553359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:22.733878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:22.922493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:23.093001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:23.280499image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:23.455033image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:23.620590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:23.796120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:23.979666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:24.169121image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:24.341660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:24.526199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:24.700740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:24.889239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:25.087708image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:25.263197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:25.458674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:26.050718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:26.230275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:26.389808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:26.555409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:26.736882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:26.905469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:27.082005image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:27.250507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:27.429067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:27.615533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:27.785077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:27.963122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:28.132745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:28.301264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:28.476794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:28.660304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:28.854783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:29.035300image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:29.228849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:29.412330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:29.596842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:29.793273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:29.977781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:30.167305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:30.342847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:30.509356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:30.668931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:30.831532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:31.013057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:31.175615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:31.347116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:31.509710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:31.683262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:31.864775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:32.028329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:32.204857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:32.477097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:32.634743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:32.813266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:32.993950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:33.189429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:33.364959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:33.553497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:33.732983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:33.925460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:34.127918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:34.320405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:34.514885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:34.696442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:34.868938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:35.059428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:35.255948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:35.453373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:35.634933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:35.832397image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:36.019860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:36.219366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:36.426814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:36.614302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:36.809790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:36.999240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:37.189731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:37.347308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:37.510907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:37.687400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:37.854992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:38.029483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:38.196059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:38.374611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:38.573047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:38.747617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:38.928143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:39.095690image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:39.253229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:39.423774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:39.606285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:39.792831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:39.970345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:40.165790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:40.486968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:40.681035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:40.881222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:41.056796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:41.248247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:41.426807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:41.594398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:41.752983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:41.918494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:42.094026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:42.263572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:42.448111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:42.614634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:42.795181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:42.983681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:43.146252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:43.322781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:43.492325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:43.646871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:43.802456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:43.960068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:44.128173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:44.282748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:44.448311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:44.605851image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:44.778391image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:44.954952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:45.113494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:45.281044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:45.437625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-04-14T23:03:52.368063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T23:03:52.740031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T23:03:53.116733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T23:03:53.483272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-14T23:03:45.834918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:46.544086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:47.112087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-04-14T23:03:47.379371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
08NaN1.062845623983514.020.0NaN161132.0NaN11.02020-07-16 16:44:00913206.02.000036e+09NaN20770562.060.0NaN13141274.0NaNNaN245.0NaN
154NaN1.062842869840109.020.0NaN161096.0NaN11.02020-07-16 13:46:00893106.02.000036e+09NaN4622690.060.0NaN81213597.0NaNNaN248.0NaN
2170NaN1.062832654NaN20.0NaN161016.0NaN11.02020-07-15 21:19:00852801.02.001933e+09NaN97728197.060.0NaN55472826.0NaNNaN248.0NaN
3173NaN1.062832258967808.020.0NaN161043.0NaN11.02020-07-16 13:25:00913206.02.000036e+09NaN1126163.060.0NaN13141274.0NaNNaN245.0NaN
4202NaN1.062828835985702.020.0NaN161016.0NaN11.02020-07-16 09:50:00852801.02.001933e+09NaN10230304.060.0NaN55472826.0NaNNaN244.0NaN
5252NaN1.062852504983514.020.0NaN161132.0NaN11.02020-07-16 16:44:00913206.02.000036e+09NaN20770562.060.0NaN13141274.0NaNNaN245.0NaN
6372NaN1.062861976817600.020.0NaN161043.0NaN11.02020-07-16 05:44:00862305.02.001931e+09NaN186424.060.0NaN160206.0NaNNaN243.0NaN
7388NaN1.062891726NaN20.0NaN161016.0NaN11.02020-07-15 21:19:00852801.02.001933e+09NaN97728197.060.0NaN55472826.0NaNNaN249.0NaN
8461NaN1.062854542967808.020.0NaN161043.0NaN11.02020-07-16 13:25:00913206.02.000036e+09NaN1126163.060.0NaN13141274.0NaNNaN245.0NaN
9496NaN1.062848973840109.020.0NaN161096.0NaN11.02020-07-16 10:40:00893106.02.000036e+09NaN4622690.060.0NaN81213597.0NaNNaN245.0NaN

Last rows

df_indexindex_trainlengthcar_numberdestination_esradmdangergruzloadedoperation_caroperation_dateoperation_st_esroperation_st_idoperation_trainreceiverrodvagrod_trainsenderssp_station_esrssp_station_idtare_weightweight_brutto
1053854189436NaN1.062602651944007.020.0NaN161096.0NaN11.02020-07-16 15:38:00944609.02.000038e+09NaN12627615.060.0NaN164517.0NaNNaN245.0NaN
1053864189484NaN1.062598461840109.020.0NaN161096.0NaN11.02020-07-16 13:46:00893106.02.000036e+09NaN4622690.060.0NaN81213597.0NaNNaN245.0NaN
1053874189494NaN1.062599253982600.020.0NaN161113.0NaN11.02020-07-16 17:59:00917207.02.000036e+09NaN97059520.060.0NaN161246.0NaNNaN245.0NaN
1053884189514NaN1.062597976967808.020.0NaN161113.0NaN11.02020-07-16 12:22:00917207.02.000036e+09NaN71479207.060.0NaN161246.0NaNNaN245.0NaN
1053894189530NaN1.062822085NaN20.0NaN161016.0NaN11.02020-07-15 21:19:00852801.02.001933e+09NaN97728197.060.0NaN55472826.0NaNNaN248.0NaN
1053904189566NaN1.062804869985702.020.0NaN161016.0NaN11.02020-07-16 09:50:00852801.02.001933e+09NaN10230304.060.0NaN55472826.0NaNNaN241.0NaN
1053914189581NaN1.062804729985702.020.0NaN161016.0NaN11.02020-07-16 09:50:00852801.02.001933e+09NaN10230304.060.0NaN55472826.0NaNNaN245.0NaN
1053924189635NaN1.062802418840109.020.0NaN161096.0NaN11.02020-07-16 10:40:00893106.02.000036e+09NaN4622690.060.0NaN81213597.0NaNNaN247.0NaN
1053934189741NaN1.062823323NaN20.0NaN161016.0NaN11.02020-07-15 21:19:00852801.02.001933e+09NaN97728197.060.0NaN55472826.0NaNNaN249.0NaN
1053944189794NaN1.062820030985702.020.0NaN161016.0NaN11.02020-07-16 06:55:00852801.02.001933e+09NaN461379.060.0NaN55472826.0NaNNaN249.0NaN